import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
     from huggingface_hub import snapshot_download
     from pyctcdecode import BeamSearchDecoderCTC

     from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
     from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
     from transformers import WavaVecaForCTC
@require_pyctcdecode
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
    UpperCamelCase			  =      dict(zip(lowerCamelCase_   ,   range(len(lowerCamelCase_				)				)				)				)

    UpperCamelCase			  =      {
        """unk_token""": """<unk>""",
        """bos_token""": """<s>""",
        """eos_token""": """</s>""",
    }
    UpperCamelCase			  =      {
        """feature_size""": 1,
        """padding_value""": 0.0,
        """sampling_rate""": 1_6000,
        """return_attention_mask""": False,
        """do_normalize""": True,
    }

    UpperCamelCase			  =      tempfile.mkdtemp()
    UpperCamelCase			  =      os.path.join(self.tmpdirname   ,   VOCAB_FILES_NAMES["""vocab_file"""]				)
    UpperCamelCase			  =      os.path.join(self.tmpdirname   ,   lowerCamelCase_				)
    with open(self.vocab_file   ,   """w"""   ,   encoding="""utf-8"""				) as fp:
     fp.write(json.dumps(lowerCamelCase_				) + """\n"""				)

    with open(self.feature_extraction_file   ,   """w"""   ,   encoding="""utf-8"""				) as fp:
     fp.write(json.dumps(lowerCamelCase_				) + """\n"""				)

    # load decoder from hub
    UpperCamelCase			  =      """hf-internal-testing/ngram-beam-search-decoder"""







   def 						lowerCamelCase_	(     self						:						Dict   ,   **lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.add_kwargs_tokens_map.copy()
    kwargs.update(lowerCamelCase_				)
    return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   **lowerCamelCase_						:						Any				):





    """simple docstring"""


    return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   **lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    shutil.rmtree(self.tmpdirname				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_tokenizer()
    UpperCamelCase			  =      self.get_feature_extractor()
    UpperCamelCase			  =      self.get_decoder()

    UpperCamelCase			  =      WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_   ,   feature_extractor=lowerCamelCase_   ,   decoder=lowerCamelCase_				)

    processor.save_pretrained(self.tmpdirname				)
    UpperCamelCase			  =      WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname				)

    # tokenizer
    self.assertEqual(processor.tokenizer.get_vocab()   ,   tokenizer.get_vocab()				)
    self.assertIsInstance(processor.tokenizer   ,   lowerCamelCase_				)

    # feature extractor
    self.assertEqual(processor.feature_extractor.to_json_string()   ,   feature_extractor.to_json_string()				)
    self.assertIsInstance(processor.feature_extractor   ,   lowerCamelCase_				)

    # decoder
    self.assertEqual(processor.decoder._alphabet.labels   ,   decoder._alphabet.labels				)
    self.assertEqual(
        processor.decoder.model_container[decoder._model_key]._unigram_set   ,   decoder.model_container[decoder._model_key]._unigram_set   ,   )
    self.assertIsInstance(processor.decoder   ,   lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      WavaVecaProcessorWithLM(
        tokenizer=self.get_tokenizer()   ,   feature_extractor=self.get_feature_extractor()   ,   decoder=self.get_decoder()				)
    processor.save_pretrained(self.tmpdirname				)

    # make sure that error is thrown when decoder alphabet doesn't match
    UpperCamelCase			  =      WavaVecaProcessorWithLM.from_pretrained(
        self.tmpdirname   ,   alpha=5.0   ,   beta=3.0   ,   score_boundary=-7.0   ,   unk_score_offset=3				)

    # decoder
    self.assertEqual(processor.language_model.alpha   ,   5.0				)
    self.assertEqual(processor.language_model.beta   ,   3.0				)
    self.assertEqual(processor.language_model.score_boundary   ,   -7.0				)
    self.assertEqual(processor.language_model.unk_score_offset   ,   3				)







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_tokenizer()
    # add token to trigger raise
    tokenizer.add_tokens(["""xx"""]				)
    with self.assertRaisesRegex(lowerCamelCase_   ,   """include"""				):
     WavaVecaProcessorWithLM(
         tokenizer=lowerCamelCase_   ,   feature_extractor=self.get_feature_extractor()   ,   decoder=self.get_decoder()				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_feature_extractor()
    UpperCamelCase			  =      self.get_tokenizer()
    UpperCamelCase			  =      self.get_decoder()

    UpperCamelCase			  =      WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_   ,   feature_extractor=lowerCamelCase_   ,   decoder=lowerCamelCase_				)

    UpperCamelCase			  =      floats_list((3, 1000)				)

    UpperCamelCase			  =      feature_extractor(lowerCamelCase_   ,   return_tensors="""np"""				)
    UpperCamelCase			  =      processor(lowerCamelCase_   ,   return_tensors="""np"""				)

    for key in input_feat_extract.keys():
     self.assertAlmostEqual(input_feat_extract[key].sum()   ,   input_processor[key].sum()   ,   delta=1E-2				)







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_feature_extractor()
    UpperCamelCase			  =      self.get_tokenizer()
    UpperCamelCase			  =      self.get_decoder()

    UpperCamelCase			  =      WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_   ,   feature_extractor=lowerCamelCase_   ,   decoder=lowerCamelCase_				)

    UpperCamelCase			  =      """This is a test string"""

    UpperCamelCase			  =      processor(text=lowerCamelCase_				)

    UpperCamelCase			  =      tokenizer(lowerCamelCase_				)

    for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]   ,   encoded_processor[key]				)







   def 						lowerCamelCase_	(     self						:						Dict   ,   lowerCamelCase_						:						Union[str, Any]=(2, 10, 16)   ,   lowerCamelCase_						:						Tuple=77				):





    """simple docstring"""


    np.random.seed(lowerCamelCase_				)
    return np.random.rand(*lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_feature_extractor()
    UpperCamelCase			  =      self.get_tokenizer()
    UpperCamelCase			  =      self.get_decoder()

    UpperCamelCase			  =      WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_   ,   feature_extractor=lowerCamelCase_   ,   decoder=lowerCamelCase_				)

    UpperCamelCase			  =      self._get_dummy_logits(shape=(10, 16)   ,   seed=13				)

    UpperCamelCase			  =      processor.decode(lowerCamelCase_				)

    UpperCamelCase			  =      decoder.decode_beams(lowerCamelCase_				)[0]

    self.assertEqual(decoded_decoder[0]   ,   decoded_processor.text				)
    self.assertEqual("""</s> <s> </s>"""   ,   decoded_processor.text				)
    self.assertEqual(decoded_decoder[-2]   ,   decoded_processor.logit_score				)
    self.assertEqual(decoded_decoder[-1]   ,   decoded_processor.lm_score				)







   @parameterized.expand([[None], ["""fork"""], ["""spawn"""]]				)
   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_feature_extractor()
    UpperCamelCase			  =      self.get_tokenizer()
    UpperCamelCase			  =      self.get_decoder()

    UpperCamelCase			  =      WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_   ,   feature_extractor=lowerCamelCase_   ,   decoder=lowerCamelCase_				)

    UpperCamelCase			  =      self._get_dummy_logits()

    # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
    #       otherwise, the LM won't be available to the pool's sub-processes.
    # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
    if pool_context is None:
     UpperCamelCase			  =      processor.batch_decode(lowerCamelCase_				)
    else:
     with get_context(lowerCamelCase_				).Pool() as pool:
      UpperCamelCase			  =      processor.batch_decode(lowerCamelCase_   ,   lowerCamelCase_				)

    UpperCamelCase			  =      list(lowerCamelCase_				)

    with get_context("""fork"""				).Pool() as p:
     UpperCamelCase			  =      decoder.decode_beams_batch(lowerCamelCase_   ,   lowerCamelCase_				)

    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      [], [], []
    for beams in decoded_beams:
     texts_decoder.append(beams[0][0]				)
     logit_scores_decoder.append(beams[0][-2]				)
     lm_scores_decoder.append(beams[0][-1]				)

    self.assertListEqual(lowerCamelCase_   ,   decoded_processor.text				)
    self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""]   ,   decoded_processor.text				)
    self.assertListEqual(lowerCamelCase_   ,   decoded_processor.logit_score				)
    self.assertListEqual(lowerCamelCase_   ,   decoded_processor.lm_score				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_feature_extractor()
    UpperCamelCase			  =      self.get_tokenizer()
    UpperCamelCase			  =      self.get_decoder()

    UpperCamelCase			  =      WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_   ,   feature_extractor=lowerCamelCase_   ,   decoder=lowerCamelCase_				)

    UpperCamelCase			  =      self._get_dummy_logits()

    UpperCamelCase			  =      15
    UpperCamelCase			  =      -2_0.0
    UpperCamelCase			  =      -4.0

    UpperCamelCase			  =      processor.batch_decode(
        lowerCamelCase_   ,   beam_width=lowerCamelCase_   ,   beam_prune_logp=lowerCamelCase_   ,   token_min_logp=lowerCamelCase_   ,   )
    UpperCamelCase			  =      decoded_processor_out.text

    UpperCamelCase			  =      list(lowerCamelCase_				)

    with get_context("""fork"""				).Pool() as pool:
     UpperCamelCase			  =      decoder.decode_beams_batch(
         lowerCamelCase_   ,   lowerCamelCase_   ,   beam_width=lowerCamelCase_   ,   beam_prune_logp=lowerCamelCase_   ,   token_min_logp=lowerCamelCase_   ,   )

    UpperCamelCase			  =      [d[0][0] for d in decoded_decoder_out]
    UpperCamelCase			  =      [d[0][2] for d in decoded_decoder_out]
    UpperCamelCase			  =      [d[0][3] for d in decoded_decoder_out]

    self.assertListEqual(lowerCamelCase_   ,   lowerCamelCase_				)
    self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""]   ,   lowerCamelCase_				)

    self.assertTrue(np.array_equal(lowerCamelCase_   ,   decoded_processor_out.logit_score				)				)
    self.assertTrue(np.allclose([-2_0.0_5_4, -1_8.4_4_7]   ,   lowerCamelCase_   ,   atol=1E-3				)				)

    self.assertTrue(np.array_equal(lowerCamelCase_   ,   decoded_processor_out.lm_score				)				)
    self.assertTrue(np.allclose([-1_5.5_5_4, -1_3.9_4_7_4]   ,   lowerCamelCase_   ,   atol=1E-3				)				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_feature_extractor()
    UpperCamelCase			  =      self.get_tokenizer()
    UpperCamelCase			  =      self.get_decoder()

    UpperCamelCase			  =      WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_   ,   feature_extractor=lowerCamelCase_   ,   decoder=lowerCamelCase_				)

    UpperCamelCase			  =      self._get_dummy_logits()

    UpperCamelCase			  =      2.0
    UpperCamelCase			  =      5.0
    UpperCamelCase			  =      -2_0.0
    UpperCamelCase			  =      True

    UpperCamelCase			  =      processor.batch_decode(
        lowerCamelCase_   ,   alpha=lowerCamelCase_   ,   beta=lowerCamelCase_   ,   unk_score_offset=lowerCamelCase_   ,   lm_score_boundary=lowerCamelCase_   ,   )
    UpperCamelCase			  =      decoded_processor_out.text

    UpperCamelCase			  =      list(lowerCamelCase_				)
    decoder.reset_params(
        alpha=lowerCamelCase_   ,   beta=lowerCamelCase_   ,   unk_score_offset=lowerCamelCase_   ,   lm_score_boundary=lowerCamelCase_   ,   )

    with get_context("""fork"""				).Pool() as pool:
     UpperCamelCase			  =      decoder.decode_beams_batch(
         lowerCamelCase_   ,   lowerCamelCase_   ,   )

    UpperCamelCase			  =      [d[0][0] for d in decoded_decoder_out]

    self.assertListEqual(lowerCamelCase_   ,   lowerCamelCase_				)
    self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""]   ,   lowerCamelCase_				)
    UpperCamelCase			  =      processor.decoder.model_container[processor.decoder._model_key]
    self.assertEqual(lm_model.alpha   ,   2.0				)
    self.assertEqual(lm_model.beta   ,   5.0				)
    self.assertEqual(lm_model.unk_score_offset   ,   -2_0.0				)
    self.assertEqual(lm_model.score_boundary   ,   lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""				)

    UpperCamelCase			  =      processor.decoder.model_container[processor.decoder._model_key]
    UpperCamelCase			  =      Path(language_model._kenlm_model.path.decode("""utf-8"""				)				).parent.parent.absolute()

    UpperCamelCase			  =      os.listdir(lowerCamelCase_				)
    UpperCamelCase			  =      ["""alphabet.json""", """language_model"""]

    downloaded_decoder_files.sort()
    expected_decoder_files.sort()

    # test that only decoder relevant files from
    # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
    # are downloaded and none of the rest (e.g. README.md, ...)
    self.assertListEqual(lowerCamelCase_   ,   lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      snapshot_download("""hf-internal-testing/processor_with_lm"""				)

    UpperCamelCase			  =      WavaVecaProcessorWithLM.from_pretrained(lowerCamelCase_				)

    UpperCamelCase			  =      processor.decoder.model_container[processor.decoder._model_key]
    UpperCamelCase			  =      Path(language_model._kenlm_model.path.decode("""utf-8"""				)				).parent.parent.absolute()

    UpperCamelCase			  =      os.listdir(lowerCamelCase_				)
    UpperCamelCase			  =      os.listdir(lowerCamelCase_				)

    local_decoder_files.sort()
    expected_decoder_files.sort()

    # test that both decoder form hub and local files in cache are the same
    self.assertListEqual(lowerCamelCase_   ,   lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""				)
    UpperCamelCase			  =      AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm"""				)

    UpperCamelCase			  =      floats_list((3, 1000)				)

    UpperCamelCase			  =      processor_wavaveca(lowerCamelCase_   ,   return_tensors="""np"""				)
    UpperCamelCase			  =      processor_auto(lowerCamelCase_   ,   return_tensors="""np"""				)

    for key in input_wavaveca.keys():
     self.assertAlmostEqual(input_wavaveca[key].sum()   ,   input_auto[key].sum()   ,   delta=1E-2				)

    UpperCamelCase			  =      self._get_dummy_logits()

    UpperCamelCase			  =      processor_wavaveca.batch_decode(lowerCamelCase_				)
    UpperCamelCase			  =      processor_auto.batch_decode(lowerCamelCase_				)

    self.assertListEqual(decoded_wavaveca.text   ,   decoded_auto.text				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_feature_extractor()
    UpperCamelCase			  =      self.get_tokenizer()
    UpperCamelCase			  =      self.get_decoder()

    UpperCamelCase			  =      WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_   ,   feature_extractor=lowerCamelCase_   ,   decoder=lowerCamelCase_				)

    self.assertListEqual(
        processor.model_input_names   ,   feature_extractor.model_input_names   ,   msg="""`processor` and `feature_extractor` model input names do not match"""   ,   )







   @staticmethod
   def 						lowerCamelCase_	(     lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      [d[key] for d in offsets]
    return retrieved_list







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""				)
    UpperCamelCase			  =      self._get_dummy_logits()[0]

    UpperCamelCase			  =      processor.decode(lowerCamelCase_   ,   output_word_offsets=lowerCamelCase_				)
    # check Wav2Vec2CTCTokenizerOutput keys for word
    self.assertEqual(len(outputs.keys()				)   ,   4				)
    self.assertTrue("""text""" in outputs				)
    self.assertTrue("""word_offsets""" in outputs				)
    self.assertTrue(isinstance(lowerCamelCase_   ,   lowerCamelCase_				)				)

    self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""]   ,   """word"""				)				)   ,   outputs.text				)
    self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]   ,   """word"""				)   ,   ["""<s>""", """<s>""", """</s>"""]				)
    self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]   ,   """start_offset"""				)   ,   [0, 2, 4]				)
    self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]   ,   """end_offset"""				)   ,   [1, 3, 5]				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""				)
    UpperCamelCase			  =      self._get_dummy_logits()

    UpperCamelCase			  =      processor.batch_decode(lowerCamelCase_   ,   output_word_offsets=lowerCamelCase_				)

    # check Wav2Vec2CTCTokenizerOutput keys for word
    self.assertEqual(len(outputs.keys()				)   ,   4				)
    self.assertTrue("""text""" in outputs				)
    self.assertTrue("""word_offsets""" in outputs				)
    self.assertTrue(isinstance(lowerCamelCase_   ,   lowerCamelCase_				)				)

    self.assertListEqual(
        [""" """.join(self.get_from_offsets(lowerCamelCase_   ,   """word"""				)				) for o in outputs["""word_offsets"""]]   ,   outputs.text				)
    self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]   ,   """word"""				)   ,   ["""<s>""", """<s>""", """</s>"""]				)
    self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]   ,   """start_offset"""				)   ,   [0, 2, 4]				)
    self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]   ,   """end_offset"""				)   ,   [1, 3, 5]				)







   @slow
   @require_torch
   @require_torchaudio
   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    import torch

    UpperCamelCase			  =      load_dataset("""common_voice"""   ,   """en"""   ,   split="""train"""   ,   streaming=lowerCamelCase_				)
    UpperCamelCase			  =      ds.cast_column("""audio"""   ,   datasets.Audio(sampling_rate=1_6000				)				)
    UpperCamelCase			  =      iter(lowerCamelCase_				)
    UpperCamelCase			  =      next(lowerCamelCase_				)

    UpperCamelCase			  =      AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""				)
    UpperCamelCase			  =      WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""				)

    # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
    UpperCamelCase			  =      processor(sample["""audio"""]["""array"""]   ,   return_tensors="""pt"""				).input_values

    with torch.no_grad():
     UpperCamelCase			  =      model(lowerCamelCase_				).logits.cpu().numpy()

    UpperCamelCase			  =      processor.decode(logits[0]   ,   output_word_offsets=lowerCamelCase_				)

    UpperCamelCase			  =      model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
    UpperCamelCase			  =      [
        {
            """start_time""": d["""start_offset"""] * time_offset,
            """end_time""": d["""end_offset"""] * time_offset,
            """word""": d["""word"""],
        }
        for d in output["""word_offsets"""]
    ]

    UpperCamelCase			  =      """WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""

    # output words
    self.assertEqual(""" """.join(self.get_from_offsets(lowerCamelCase_   ,   """word"""				)				)   ,   lowerCamelCase_				)
    self.assertEqual(""" """.join(self.get_from_offsets(lowerCamelCase_   ,   """word"""				)				)   ,   output.text				)

    # output times
    UpperCamelCase			  =      torch.tensor(self.get_from_offsets(lowerCamelCase_   ,   """start_time"""				)				)
    UpperCamelCase			  =      torch.tensor(self.get_from_offsets(lowerCamelCase_   ,   """end_time"""				)				)

    # fmt: off
    UpperCamelCase			  =      torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9]				)
    UpperCamelCase			  =      torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4]				)
    # fmt: on

    self.assertTrue(torch.allclose(lowerCamelCase_   ,   lowerCamelCase_   ,   atol=0.0_1				)				)
    self.assertTrue(torch.allclose(lowerCamelCase_   ,   lowerCamelCase_   ,   atol=0.0_1				)				)

def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	List[str]:







 '''simple docstring'''
 _enforce_args(UpperCamelCase_							,					UpperCamelCase_       )
 if n == 0:
  return 0
 UpperCamelCase			  =      float("""-inf"""       )
 for i in range(1							,					n + 1       ):
  UpperCamelCase			  =      max(
      UpperCamelCase_							,					prices[i - 1] + naive_cut_rod_recursive(n - i							,					UpperCamelCase_       )       )

 return max_revue





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Optional[Any]:







 '''simple docstring'''
 _enforce_args(UpperCamelCase_							,					UpperCamelCase_       )
 UpperCamelCase			  =      [float("""-inf"""       ) for _ in range(n + 1       )]
 return _top_down_cut_rod_recursive(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	List[Any]:







 '''simple docstring'''
 if max_rev[n] >= 0:
  return max_rev[n]
 elif n == 0:
  return 0
 else:
  UpperCamelCase			  =      float("""-inf"""       )
  for i in range(1							,					n + 1       ):
   UpperCamelCase			  =      max(
       UpperCamelCase_							,					prices[i - 1] + _top_down_cut_rod_recursive(n - i							,					UpperCamelCase_							,					UpperCamelCase_       )							,					)

  UpperCamelCase			  =      max_revenue

 return max_rev[n]





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Union[str, Any]:







 '''simple docstring'''
 _enforce_args(UpperCamelCase_							,					UpperCamelCase_       )

 # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
 # length 0.
 UpperCamelCase			  =      [float("""-inf"""       ) for _ in range(n + 1       )]
 UpperCamelCase			  =      0

 for i in range(1							,					n + 1       ):
  UpperCamelCase			  =      max_rev[i]
  for j in range(1							,					i + 1       ):
   UpperCamelCase			  =      max(UpperCamelCase_							,					prices[j - 1] + max_rev[i - j]       )

  UpperCamelCase			  =      max_revenue_i

 return max_rev[n]





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Union[str, Any]:







 '''simple docstring'''
 if n < 0:
  UpperCamelCase			  =      f"""n must be greater than or equal to 0. Got n = {n}"""
  raise ValueError(UpperCamelCase_       )

 if n > len(UpperCamelCase_       ):
  UpperCamelCase			  =      (
      """Each integral piece of rod must have a corresponding price. """
      f"""Got n = {n} but length of prices = {len(UpperCamelCase_       )}"""
  )
  raise ValueError(UpperCamelCase_       )





def 			lowercase(	)		->	str:







 '''simple docstring'''
 UpperCamelCase			  =      [6, 10, 12, 15, 20, 23]
 UpperCamelCase			  =      len(UpperCamelCase_       )

 # the best revenue comes from cutting the rod into 6 pieces, each
 # of length 1 resulting in a revenue of 6 * 6 = 36.
 UpperCamelCase			  =      36

 UpperCamelCase			  =      top_down_cut_rod(UpperCamelCase_							,					UpperCamelCase_       )
 UpperCamelCase			  =      bottom_up_cut_rod(UpperCamelCase_							,					UpperCamelCase_       )
 UpperCamelCase			  =      naive_cut_rod_recursive(UpperCamelCase_							,					UpperCamelCase_       )

 assert expected_max_revenue == max_rev_top_down
 assert max_rev_top_down == max_rev_bottom_up
 assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
     main()
